3D Tracking Using Multi-view Based Particle Filters
Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios.
KeywordsParticle Filter Multiple Camera Motion Segmentation Visual Surveillance Multiple People
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- 2.Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: Proc. Workshop on Motion and Video Computing, pp. 169–174 (2002)Google Scholar
- 5.Focken, D., Stiefelhagen, R.: Towards vision-based 3-D people tracking in a smart room. In: Proc. IEEE Int. Conf. Multimodal Interfaces, pp. 400–405 (2002)Google Scholar
- 6.Gonzalez, R.I., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Englewood Cliffs (2008)Google Scholar
- 10.Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M.: Multi-camera multi-person tracking for easy living. In: IEEE Int. Workshop on Visual Surveillance (2000)Google Scholar